An initial investigation in the diagnosis of Alzheimer's disease using various classification techniques

Abstract

Now a day's most of the people suffer from brain related neurodegenerative disorders. These disorders lead to various diseases. Dementia is one such disease. Dementia is a general term for a decline in mental ability severe enough to interfere with daily life. Alzheimer's disease is the most common type of dementia. Alzheimer's disease is one of the types of the dementia which accounts to 60–80% of mental disorders [1]. Diagnosis of the disease at the earlier stage is a crucial task. Diagnosis of the disease at the early stage will enable the diseased to have quality life. Authors have collected data from various neuropsychologists which consist of 250 patient's records. In this paper the authors focus on diagnosis of disease using various machine learning techniques of data mining. Authors have compared various classification techniques such as Naive Bayes, Decision tree algorithm J48, Random forest, JRIP and suggest Naïve bayes as the best technique.

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